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A Comprehensive Review of Image Restoration Research Based on Diffusion Models

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  • Jun Li

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
    Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China
    These authors contributed equally to this work.)

  • Heran Wang

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
    These authors contributed equally to this work.)

  • Yingjie Li

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China)

  • Haochuan Zhang

    (School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China)

Abstract

Image restoration is an indispensable and challenging task in computer vision, aiming to enhance the quality of images degraded by various forms of degradation. Diffusion models have achieved remarkable progress in AIGC (Artificial Intelligence Generated Content) image generation, and numerous studies have explored their application in image restoration, achieving performance surpassing that of other methods. This paper provides a comprehensive overview of diffusion models for image restoration, starting with an introduction to the background of diffusion models. It summarizes relevant theories and research in utilizing diffusion models for image restoration in recent years, elaborating on six commonly used methods and their unified paradigm. Based on these six categories, this paper classifies restoration tasks into two main areas: image super-resolution reconstruction and frequency-selective image restoration. The frequency-selective image restoration category includes image deblurring, image inpainting, image deraining, image desnowing, image dehazing, image denoising, and low-light enhancement. For each area, this paper delves into the technical principles and modeling strategies. Furthermore, it analyzes the specific characteristics and contributions of the diffusion models employed in each application category. This paper summarizes commonly used datasets and evaluation metrics for these six applications to facilitate comprehensive evaluation of existing methods. Finally, it concludes by identifying the limitations of current research, outlining challenges, and offering perspectives on future applications.

Suggested Citation

  • Jun Li & Heran Wang & Yingjie Li & Haochuan Zhang, 2025. "A Comprehensive Review of Image Restoration Research Based on Diffusion Models," Mathematics, MDPI, vol. 13(13), pages 1-37, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2079-:d:1686064
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    References listed on IDEAS

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    1. van Dyk, David A. & Park, Taeyoung, 2008. "Partially Collapsed Gibbs Samplers: Theory and Methods," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 790-796, June.
    2. Hafiz Syed Muhammad Muslim & Sajid Ali Khan & Shariq Hussain & Arif Jamal & Hafiz Syed Ahmed Qasim, 2019. "A knowledge-based image enhancement and denoising approach," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 108-121, June.
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